Robust Convergency Indicator using High-dimension PID Controller in the presence of disturbance
The PID controller currently occupies a prominent position as the most prevalent control architecture, which has achieved groundbreaking success across extensive implications. However, its parameters online regulation remains a formidable challenge. The majority of existing theories hinge on the linear constant system structure, contemplating only Single-Input, Single-Output (SISO) scenarios. Restricted research has been conducted on the intricate PID control problem within high-dimensional, Multi-Input, Multi-Output (MIMO) nonlinear systems that incorporate disturbances. This research, providing insights on the velocity form of nonlinear system, aims to bolster the controller's robustness. It establishes a quantitative metric to assess the robustness of high-dimensional PID controller, elucidates the pivotal theory regarding robustness's impact on error exponential convergence, and introduces a localized compensation strategy to optimize the robustness indicator. Guided by these theoretical insights, we exploit a robust high-dimensional PID (RH-PID) controller without the crutch of oversimplifying assumptions. Experimental results demonstrate the controller's commendable exponential stabilization efficacy and the controller exhibits exceptional robustness under the robust indicator's guidance. Notably, the robust convergence indicator can also effectively evaluate the comprehensive performance.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Generative Attribute Controller With Conditional Filtered Generative Adversarial Networks
We present a generative attribute controller (GAC), a novel functionality for generating or editing an image while intuitively controlling large variations of an attribute. This controller is based on a novel generative …
AttributeGenerative Adversarial NetworkImage RetrievalRetrievalAn Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy
Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multi-objective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. Ho…
ClusteringEvolutionary AlgorithmsSAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency
Recently, the dominant DETR-based approaches apply central-concept spatial prior to accelerate Transformer detector convergency. These methods gradually refine the reference points to the center of target objects and imb…
Objectobject-detectionObject DetectionGOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering
It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be direct…
Koopman-Hopf Hamilton-Jacobi Reachability and Control
The Hopf formula for Hamilton-Jacobi reachability (HJR) analysis has been proposed to solve high-dimensional differential games, producing the set of initial states and corresponding controller required to reach (or avoi…